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Zama CEO 称全同态加密 GPU 基准测试达每秒 1000 笔机密转账,计划年底上线主网

火星财经消息,全同态加密(FHE)公司 Zama CEO Rand Hindi 周一在 X 上表示,公司在 GPU 上实现每秒 1000 笔机密转账的基准测试结果,较 2022 年 ETHcc 首次演示时的每秒 0.2 笔提升约 5000 倍。Hindi 称运行 FHE 节点的成本约为每笔 ERC7984 交易 0.000004 美元,远低于零知识证明,但该对比未经独立验证。该基准尚未在任何主网上激活,属公司自报数据且未公布测试方法,计划年底上线。Hindi 还设定远期目标:2028 年达 10,000 tps、2030 年达 100,000 tps。Zama 近期动作密集:7 月 24 日机密 RFQ 交换产品上线,上线数周内在以太坊上屏蔽逾 1.21 亿美元 USDT,与 Elliptic 合作为机密交易增加合规筛查,并与 OpenZeppelin 及 Inco 共同成立机密代币协会以标准化加密 ERC-20。Zama 此前完成 5700 万美元 B 轮融资。
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07-02 11:41

Nvidia provides financing support for GPU procurement and takes a percentage of its cloud computing revenue.

Nvidia reportedly provides financing support for GPU procurement and takes a percentage of its cloud computing revenue. (Cailian Press)

07-04 19:39

Tether CEO warns AI giants' computing power subsidy model: Multiple cycles of mismatch continue to accumulate industry risks.

On July 4th, PANews reported that Tether CEO Paolo Ardoino published an article on the X platform, questioning the current expansion model of AI giants subsidizing computing power in exchange for user scale. He stated that leading global AI technology companies are continuously increasing their investment in computing infrastructure to seize market share, resulting in huge capital expenditures. However, the economic depreciation cycle of computing assets such as GPUs and servers is only 3 to 5 years, and the hardware depreciates extremely quickly. This creates structural mismatch risks: token prices are decoupled from the real value of assets, the profit realization cycle lags behind the capital investment cycle, and the cost of capital does not match the debt repayment period. At the same time, open-source AI models continue to divert market demand and compress commercial revenue space.

06-22 21:11

NVIDIA unveils Vera Rubin platform: a rack-mount supercomputer that will deliver 7 Exaflops of AI computing power.

Mars Finance reported on June 22 that NVIDIA announced the launch of the Vera Rubin supercomputing platform, targeting scientific computing and high-performance computing (HPC). A single-rack system can provide over 7 Exaflops of AI computing power and 5 Petaflops of FP64 double-precision performance, described by NVIDIA as "achieving TOP500-level supercomputing capabilities in a single rack." The platform integrates the Rubin GPU and Vera CPU architectures, combined with technologies such as NVLink-C2C, ConnectX-9 SuperNIC, and BlueField-4 DPU, supporting the unified computing needs of AI training, scientific simulation, and data-intensive research. NVIDIA stated that Vera Rubin will be primarily applied to highly complex scientific computing tasks such as climate modeling, computational fluid dynamics, quantum chemistry, and energy exploration, and will support an integrated workflow of "AI + simulation + data analysis." At the application level, institutions such as the Leibniz Supercomputing Center, the National Energy Research Scientific Computing Center (NERSC) of the U.S. Department of Energy, and Los Alamos National Laboratory have planned to use this platform to build next-generation supercomputing systems. In addition, companies such as Bull, Dell Technologies, HPE, and Supermicro will also launch high-density liquid-cooled rack-mount supercomputing solutions based on Vera Rubin. NVIDIA CEO Jensen Huang stated that the platform aims to integrate AI, simulation, and data processing into "new scientific tools" to accelerate scientific discovery and industrial innovation.

06-22 21:08

Europe has announced the deployment of 35 NVIDIA AI supercomputers, creating the region's largest expansion of AI computing power.

According to Mars Finance, on June 22, NVIDIA announced that it is building or deploying 35 AI and HPC supercomputers in Europe, covering 23 countries and research institutions, marking the largest supercomputing expansion in Europe within a year. These systems are based on NVIDIA Blackwell and Hopper architectures and combine CUDA-X, NVIDIA NIM, and the AI Enterprise software stack to support fields such as climate science, medical research, clean energy, and quantum computing. Official data shows that the infrastructure will serve more than 3 million researchers. NVIDIA stated that its AI infrastructure already supports the construction of over 90% of AI factories in Europe, with a cumulative deployment or planning of approximately 800 AI exaflops of computing power. In terms of specific projects, institutions such as the Barcelona Supercomputing Center, HLRS in Germany, CINECA in Italy, and NAISS in Sweden are all advancing the construction of AI factories based on NVIDIA GPUs, with some systems achieving tens of exaflops in training and inference performance. In addition, the plan includes collaboration with Siemens Energy on the development of hydrogen gas turbines, and research on quantum and GPU fusion computing based on the CUDA-Q platform. NVIDIA CEO Jensen Huang stated that AI is becoming a "new scientific instrument" that will help researchers accelerate the simulation of complex systems and scientific discovery.

06-21 22:06Important

Bernstein senior analyst: The first true chip supercycle is coming; the "bottleneck" is the real wealth-creating machine.

According to Mars Finance, on June 21, Bernstein's star chip analyst Stacy Rasgon stated that this was the first time in his 18 years in the industry that he had truly witnessed a semiconductor supercycle. Rasgon, who holds a PhD from MIT and is an engineer by training, provided some astonishing data: the semiconductor industry's total revenue exceeded $800 billion last year and is heading towards $1.3 trillion this year, with all sub-sectors, from accelerators to memory, semiconductor equipment, network optical communications, power chips, and even CPUs, experiencing a comprehensive shortage of supply. "The only consensus we're hearing right now is that no one has enough computing power. Take memory as an example: HBMs (High-density Memory) may account for over 85% of the silicon wafer area in AI chips, and the silicon wafer area required to manufacture 1GB of HBM is about four times that of standard DRAM. This means that even if wafer fabs expand production at breakneck speed, the actual increase in storage capacity will still be extremely limited. This supply-demand mismatch has even benefited Intel—its inventory, which had already been written off to zero, was snapped up. Customers' attitude was, 'We don't care, please sell to us.'" Rasgon pointed out that the industry's core focus is shifting from model training to AI inference, which is the key to commercial monetization—training the model itself doesn't generate revenue; only using the model can generate income. Anthropic data shows that annualized revenue surged from approximately $9 billion in December last year to $30 billion in April this year, almost a vertical increase. In the competitive chip landscape, the competition between custom ASICs, represented by Broadcom, and Nvidia GPUs is not a zero-sum game. "The real pain point is whether the opportunity is still growing—if it's big enough, both will thrive." Broadcom currently expects its AI revenue to reach $100 billion next year, with ASICs accounting for about ten percent of the AI chip market revenue, potentially rising to 25%-30% in the future, but not completely replacing GPUs. Regarding inference chip startups like Groq, recently acquired by Nvidia, Rasgon quoted Jensen Huang's assessment: not all tokens are created equal; low-latency tokens are more valuable, and GPUs are not the optimal choice for all tasks. When asked about the most overlooked risk in the industry, Rasgon shifted the focus from silicon wafers back to the physical world—electricity. It is estimated that if Nvidia's predicted annual infrastructure investment of $3 trillion to $4 trillion comes to fruition, the US power grid will need to expand by about 5% annually, a 5% annual growth rate that power industry analysts consider almost impossible. This means the next bottleneck will fall on energy generation, cooling, and nuclear power. "But never underestimate human creativity; engineers can always find a way when there's profit to be made." Regarding Intel, the new CEO Chen Liwu's pragmatic strategy of low expectations, the better-than-expected yield of the new 18A chip's process, and government and Nvidia's investment have significantly alleviated previous market concerns about its balance sheet. Rasgon concluded that as long as AI demand doesn't collapse, the supercycle across the entire industry chain will continue, and the capital market's focus should be on the capacity bottlenecks that exist at each stage.

07-08 17:46

Yiyuan Winery makes a strategic investment in Wang Defang, a time-honored brand in Guizhou.

Mars Finance reported on July 8th that Yiyuan Winery, through its wholly-owned subsidiary Chengdu Yiniang Winery, has strategically invested in the Guizhou time-honored brand "Wang Defang" liquor. Details of the transaction have not yet been disclosed. It is understood that Wang Defang is the core founder of Ronghe Distillery, one of the "three major distilleries" of Maotai Town. In May of this year, Yiyuan Winery registered six subsidiaries in Chengdu, covering brand operation, production and processing, supply chain platform, e-commerce business, store channels, and digital marketing. (Securities Times)